{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# dog_vs_cat\n",
    "1. 创建数据集\n",
    "2. 数据预处理\n",
    "3. 定义模型\n",
    "4. 提取特征并存为h5文件\n",
    "5. h5特征训练分类器 \n",
    "6. 评估模型性能"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 创建数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import shutil\n",
    "import torch\n",
    "import h5py\n",
    "import numpy as np\n",
    "from torch import nn, optim\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm_notebook\n",
    "import matplotlib.pyplot as plt\n",
    "from torch.autograd import Variable\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "from torchvision import datasets, transforms\n",
    "from torchvision import models\n",
    "from torchvision.utils import save_image\n",
    "from torchvision.datasets import ImageFolder\n",
    "\n",
    "use_cuda = torch.cuda.is_available()                    # gpu可用\n",
    "device = torch.device('cuda' if use_cuda else 'cpu')    # 优先使用gpu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data path: ..\\data\\dog_vs_cat\n"
     ]
    }
   ],
   "source": [
    "root_path = os.path.join('..', 'data', 'dog_vs_cat')     # 数据集的根目录     \n",
    "source_path = os.path.join(root_path, 'kaggle_train')    # 原图像的目录\n",
    "print('Data path:', root_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 创建dog和cat的目录"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('dog', 'train'), ('dog', 'val'), ('cat', 'train'), ('cat', 'val')]\n"
     ]
    }
   ],
   "source": [
    "def create_dir(class_name, phase):\n",
    "    \"\"\"\n",
    "    class_name：类别名称，字符串\n",
    "    phase：train或val\n",
    "    \"\"\"\n",
    "    img_folder = os.path.join(root_path, phase, class_name)\n",
    "    if not os.path.exists(img_folder):     \n",
    "        os.mkdir(img_folder)           # 只能创建一个文件夹，无法创建嵌套的文件夹\n",
    "        print(img_folder)\n",
    "    \n",
    "dir_list = [(i,j) for i in ['dog', 'cat'] for j in ['train', 'val']]  # 分为训练集和验证集\n",
    "print(dir_list)\n",
    "operation = list(map(lambda x: create_dir(*x), dir_list))             # 创建4个对应的目录"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 将图像存放到对应路径"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['cat.0.jpg', 'cat.1.jpg', 'cat.10.jpg', 'cat.100.jpg', 'cat.1000.jpg', 'cat.10000.jpg', 'cat.10001.jpg']\n"
     ]
    }
   ],
   "source": [
    "data_list = os.listdir(source_path)   # 列出原数据集中文件名，列表类型\n",
    "print(data_list[:7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['dog.0.jpg', 'dog.1.jpg', 'dog.10.jpg', 'dog.100.jpg', 'dog.1000.jpg', 'dog.10000.jpg', 'dog.10001.jpg']\n",
      "['cat.0.jpg', 'cat.1.jpg', 'cat.10.jpg', 'cat.100.jpg', 'cat.1000.jpg', 'cat.10000.jpg', 'cat.10001.jpg']\n"
     ]
    }
   ],
   "source": [
    "dog_list = list(filter(lambda x:x[:3]=='dog', data_list))     # 筛选出dog类型的文件 列表数据\n",
    "print(dog_list[:7])\n",
    "cat_list = list(filter(lambda x:x[:3]=='cat', data_list))     # 筛选cat类型文件 \n",
    "print(cat_list[:7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e80145c6dd0e41f083272cd5823ea32c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=12500), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "387d865fdae54c8ea575e332aaba1224",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=12500), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# 运行该程序会显示进度条\n",
    "def copy_data(root_path, file_list, target_class):\n",
    "    \"\"\"\n",
    "    root_path：文件的根目录\n",
    "    file_list：需要复制的文件的list\n",
    "    target_class：需要复制的文件所属类别\n",
    "    \"\"\"\n",
    "    for i in tqdm_notebook(range(len(file_list))):                  # tqdm为了显示进度条\n",
    "        source_path = os.path.join(root_path, 'kaggle_train', file_list[i])\n",
    "#         print(source_path)\n",
    "        phase = 'train' if i < len(dog_list)*0.9 else 'val'         # 选取90%数据为训练，10%验证  \n",
    "        target_path = os.path.join(root_path, phase, target_class, file_list[i])  # 创建目标目录\n",
    "#         print(target_path)\n",
    "        if not os.path.exists(target_path):         # 如果目标文件不存在\n",
    "            shutil.copy(source_path, target_path)   # 复制文件到指定目录\n",
    "copy_data(root_path, cat_list, 'cat')  \n",
    "copy_data(root_path, dog_list, 'dog')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 显示样本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1d74707d860>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d73fcc7f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "dog_sample = np.random.choice(dog_list, size=1)    # 从列表汇总随机选取一个样本\n",
    "cat_sample = np.random.choice(cat_list, size=1)\n",
    "dog_img = plt.imread(os.path.join(root_path, 'kaggle_train', dog_sample[0]))# (191, 119, 3)\n",
    "# print(dog_img.shape)\n",
    "cat_img = plt.imread(os.path.join(root_path, 'kaggle_train', cat_sample[0]))\n",
    "fig = plt.figure(figsize=(6,3))\n",
    "fig.add_subplot(121)\n",
    "plt.axis('off')\n",
    "plt.imshow(dog_img)\n",
    "fig.add_subplot(122)\n",
    "plt.axis('off')\n",
    "plt.imshow(cat_img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 2. 数据预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cat': 0, 'dog': 1}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 定义数据预处理方法\n",
    "img_transform = transforms.Compose([\n",
    "    transforms.Resize(250),                             # 缩放到250*250\n",
    "    transforms.CenterCrop(224),                         # 裁剪中心224*224尺寸的图像\n",
    "    transforms.ToTensor(),                              # 转换为[0,1]的向量\n",
    "    transforms.Normalize((0.5,0.5,0.5), (0.5,0.5,0.5))  # 转换为[-1,1]，数据零均值处理\n",
    "])\n",
    "def datafolder(phase):        \n",
    "    \"\"\"\n",
    "    func：读取数据并返回ImageFolder类\n",
    "    phase：train或者val\n",
    "    \"\"\"\n",
    "    data = ImageFolder(os.path.join(root_path, phase), transform=img_transform)\n",
    "#     print(data)\n",
    "    return data\n",
    "da = datafolder('val')\n",
    "da.class_to_idx                       # ImageFolder函数为不同名称文件夹自动分配了类别号"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 生成批次数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dataloader(data_folder, batch_size, phase='train'):\n",
    "    \"\"\"\n",
    "    func：读取数据并返回DataLoader类\n",
    "    data_folder：数据的ImageFolder\n",
    "    batch_size：批次的大小\n",
    "    phase：train或者val\n",
    "    \"\"\"    \n",
    "    # 这里不打乱顺序，主要是为了后续的特征拼接\n",
    "    loader = DataLoader(data_folder, batch_size=batch_size, shuffle=False, num_workers=4)\n",
    "    return loader  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader = dataloader(datafolder('train'), batch_size, 'train')   # 创建数据迭代器\n",
    "val_loader = dataloader(datafolder('val'), batch_size, 'val')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train numbers: 22500\n",
      "val numbers: 2500\n"
     ]
    }
   ],
   "source": [
    "print('train numbers:', len(train_loader.dataset))\n",
    "print('val numbers:', len(val_loader.dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([10, 3, 224, 224]) torch.Size([10])\n"
     ]
    }
   ],
   "source": [
    "val_data = iter(val_loader)\n",
    "x, y = next(val_data)\n",
    "print(x.shape, y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 定义模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 定义提取特征的模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2.1定义模型来提取特征，为了提取特征向量\n",
    "class feature_net(nn.Module):\n",
    "    def __init__(self, model_name):\n",
    "        super().__init__()\n",
    "        self.model_name = model_name                                        # 添加名字属性，便于识别\n",
    "        assert model_name in ['inceptionv3', 'resnet18', 'squeeze'], 'Wrong model name!'\n",
    "        if model_name == 'inceptionv3':\n",
    "            inception = models.inception_v3(pretrained=True)                # 使用预训练模型\n",
    "            self.features = nn.Sequential(*list(inception.children())[:-1]) # 除去最后一层\n",
    "            self.features._modules.pop('13')                                # 移除13层辅助分类层\n",
    "            # 网络最后输出(N,2048,26,26) => [N, 2048, 1, 1] \n",
    "            self.features.add_module('global average', nn.AvgPool2d(26))    # get (N, 2048, 1,1)\n",
    "        elif model_name == 'resnet18':\n",
    "            resnet = models.resnet18(pretrained=True)\n",
    "            self.features = nn.Sequential(*list(resnet.children())[:-1])    # (N, 512, 1, 1)\n",
    "        elif model_name == 'squeeze':\n",
    "            squeezenet = models.squeezenet1_1(pretrained=True)\n",
    "            self.features = nn.Sequential(*list(squeezenet.children())[:-1]) # (N, 512, 13, 13)\n",
    "            self.features.add_module('global average', nn.AvgPool2d(13))     # (N, 512, 1, 1)\n",
    "    def forward(self, x):\n",
    "        features = self.features(x)  # 提取特征\n",
    "        features_flatten = features.view(x.size(0), -1)\n",
    "        return features_flatten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 512])\n"
     ]
    }
   ],
   "source": [
    "feature_model = feature_net('resnet18')   # 模型测试\n",
    "test_in = torch.randn(1,3,224,224)\n",
    "out = feature_model(test_in)\n",
    "print(out.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 定义分类模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "class classifier(nn.Module):\n",
    "    def __init__(self, in_features, out_classes):\n",
    "        super().__init__()\n",
    "        self.fc = nn.Sequential(\n",
    "            nn.Linear(in_features, 512),           # (N,100)\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.5),\n",
    "            nn.Linear(512, out_classes)            # (N,out_classes)\n",
    "        )\n",
    "    def forward(self, x):\n",
    "        out = self.fc(x)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([10, 2])\n"
     ]
    }
   ],
   "source": [
    "classifier_model = classifier(200, 2)   # 模型测试\n",
    "test_in = torch.randn(10, 200)\n",
    "out = classifier_model(test_in)\n",
    "print(out.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 4.提取特征并存为h5文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义提取特征函数\n",
    "def CreateFeature(model, dataloader, phase, save_path='./feature_h5'):\n",
    "    \"\"\"\n",
    "    model：用于提取特征的模型\n",
    "    dataloader：train、val时使用的数据迭代器\n",
    "    phase：train或val\n",
    "    save_path：特征h5文件保存的路径\n",
    "    \"\"\"\n",
    "    model.to(device)                               # 优先使用GPU\n",
    "    feature_map = torch.FloatTensor().to(device)\n",
    "    label_map = torch.LongTensor().to(device)\n",
    "    idx = 0\n",
    "    for data in tqdm_notebook(dataloader):         # 显示进度条\n",
    "        img, label = data\n",
    "#         print(img.shape)\n",
    "        img, label = map(lambda x:x.to(device), [img, label])   # 选择设备，优先使用GPU\n",
    "        out = model(img)                           # 提取特征\n",
    "        feature_map = torch.cat([feature_map, out.data], 0)     # 在批次方向拼接 (N,-1)\n",
    "        label_map = torch.cat([label_map, label], 0)\n",
    "#         print(feature_map.shape)\n",
    "#         print(label_map.shape)\n",
    "#         print('--------------')\n",
    "#         idx += 1\n",
    "#         if idx == 2:\n",
    "#             break\n",
    "#     feature_map = feature_map.cpu().numpy()  # \n",
    "    label_map = label_map.cpu().numpy()\n",
    "    file_name = '{}_{}_feature.hd5f'.format(model.model_name, phase)      # h5文件名称\n",
    "    h5_path = os.path.join(save_path, file_name)\n",
    "#     print(h5_path)\n",
    "    with h5py.File(h5_path, 'w') as h5_file:\n",
    "        h5_file.create_dataset('data', data=feature_map)\n",
    "        h5_file.create_dataset('label', data=label_map)\n",
    "    print('Finished!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 创建模型resnet并提取特征\n",
    "resnet = feature_net('resnet18')\n",
    "if not os.path.exists(os.path.join('./feature_h5/', 'resnet18_train_feature.hd5f')): # 如果存在就不提特征\n",
    "    CreateFeature(resnet, train_loader, 'train')\n",
    "if not os.path.exists(os.path.join('./feature_h5/', 'resnet18_val_feature.hd5f')): # 如果存在就不提特征\n",
    "    CreateFeature(resnet, val_loader, 'val')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\torchvision\\models\\squeezenet.py:94: UserWarning: nn.init.kaiming_uniform is now deprecated in favor of nn.init.kaiming_uniform_.\n",
      "  init.kaiming_uniform(m.weight.data)\n",
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\torchvision\\models\\squeezenet.py:92: UserWarning: nn.init.normal is now deprecated in favor of nn.init.normal_.\n",
      "  init.normal(m.weight.data, mean=0.0, std=0.01)\n"
     ]
    }
   ],
   "source": [
    "# 创建模型squeezenet并提取特征\n",
    "squeezenet = feature_net('squeeze')\n",
    "if not os.path.exists(os.path.join('./feature_h5/', 'squeeze_train_feature.hd5f')): # 如果存在就不提特征\n",
    "    CreateFeature(squeezenet, train_loader, 'train')\n",
    "if not os.path.exists(os.path.join('./feature_h5/', 'squeeze_val_feature.hd5f')): # 如果存在就不提特征\n",
    "    CreateFeature(squeezenet, val_loader, 'val')    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>生成的特征h5文件</center>\n",
    "<img src='image/h5_file.jpg' height='45%', width='45%'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 5. h5特征训练分类器"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 读取h5数据并按照特征维度拼接"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义h5数据读取类，将不同模型的h5特征进行拼接\n",
    "class h5Dataset(Dataset):\n",
    "    def __init__(self, file_list):\n",
    "        \"\"\"\n",
    "        file_list：需要拼接的h5文件路径列表\n",
    "        \"\"\"\n",
    "        h5_data = torch.FloatTensor()\n",
    "        for file in file_list:\n",
    "            with h5py.File(file, 'r') as temp_file:\n",
    "                # 读取图像和标签\n",
    "                temp_data = temp_file['data'].value\n",
    "                # 转换为torch数据\n",
    "                temp_data = torch.from_numpy(temp_data)\n",
    "                if h5_data.shape == torch.Size([0]):                # 首次读取数据\n",
    "                    self.label = torch.from_numpy(temp_file['label'].value)  # 保存标签\n",
    "                    \n",
    "                h5_data = torch.cat([h5_data, temp_data], 1)        # 在列上拼接                 \n",
    "        self.data  = h5_data                                        # 保存特征数据\n",
    "#         print(self.data.shape, self.label.shape)\n",
    "        \n",
    "    def __len__(self):                                # 返回一个python常数\n",
    "        return self.label.shape[0]                    # torch.Size([22500])，使用[0]提取出数\n",
    "    \n",
    "    def __getitem__(self, index):                     # 按索引读取数据\n",
    "        assert index < len(self), 'index range error'\n",
    "        data = self.data[index]\n",
    "        label = self.label[index]\n",
    "        return (data, label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train numbers: 22500\n",
      "Sample: tensor([0.6055, 0.1336, 0.0773,  ..., 0.0970, 2.7742, 0.3208]) tensor(0)\n",
      "torch.Size([1024]) torch.Size([])\n"
     ]
    }
   ],
   "source": [
    "# 生成训练集的dataset  x:[22500,1024] y:[22500]\n",
    "train_h5_list = [os.path.join('./feature_h5', 'resnet18_train_feature.hd5f'),\n",
    "                 os.path.join('./feature_h5', 'squeeze_train_feature.hd5f')]\n",
    "train_h5 = h5Dataset(train_h5_list)\n",
    "print('Train numbers:', train_h5.__len__())\n",
    "sample_data, sample_label = train_h5.__getitem__(0)\n",
    "print('Sample:',sample_data, sample_label)\n",
    "print(sample_data.shape, sample_label.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val numbers: 2500\n"
     ]
    }
   ],
   "source": [
    "# 生成验证集的dataset   x:[2500,1024] y:[2500]\n",
    "val_h5_list = [os.path.join('./feature_h5', 'resnet18_val_feature.hd5f'),\n",
    "                 os.path.join('./feature_h5', 'squeeze_val_feature.hd5f')]\n",
    "val_h5 = h5Dataset(val_h5_list)\n",
    "print('Val numbers:', val_h5.__len__())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 生成批次训练数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_batch = 32  # 与开头的批次不同，这里的用于训练分类器\n",
    "train_h5_loader = DataLoader(train_h5, batch_size=train_batch, shuffle=True)   # 使用num_workers会报错\n",
    "val_h5_loader = DataLoader(val_h5, batch_size=train_batch, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 1024]) torch.Size([32])\n"
     ]
    }
   ],
   "source": [
    "val_h5_data = iter(val_h5_loader)\n",
    "x, y = next(val_h5_data)\n",
    "print(x.shape, y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3. 训练分类器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "classifier_model = classifier(1024, 2)   # 训练分类器,1024是特征的维度\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(classifier_model.parameters(), lr=1e-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------Epoch:1--------------------\n",
      "Loss: 0.3759, Acc: 0.8256\n",
      "Loss: 0.2892, Acc: 0.8750\n",
      "Loss: 0.2337, Acc: 0.9023\n",
      "Loss: 0.2047, Acc: 0.9150\n",
      "Loss: 0.1791, Acc: 0.9267\n",
      "Loss: 0.1618, Acc: 0.9341\n",
      "Loss: 0.1479, Acc: 0.9406\n",
      "Loss: 0.1370, Acc: 0.9452\n",
      "Loss: 0.1274, Acc: 0.9492\n",
      "Loss: 0.1193, Acc: 0.9528\n",
      "Loss: 0.1121, Acc: 0.9561\n",
      "Loss: 0.1071, Acc: 0.9582\n",
      "Loss: 0.1015, Acc: 0.9606\n",
      "Loss: 0.0970, Acc: 0.9625\n",
      "Loss: 0.0966, Acc: 0.9627, Time: 4s\n",
      "Validation: Loss: 0.0390, Acc: 0.9856\n",
      "--------------------Epoch:2--------------------\n",
      "Loss: 0.0233, Acc: 0.9956\n",
      "Loss: 0.0194, Acc: 0.9959\n",
      "Loss: 0.0201, Acc: 0.9950\n",
      "Loss: 0.0205, Acc: 0.9947\n",
      "Loss: 0.0199, Acc: 0.9946\n",
      "Loss: 0.0200, Acc: 0.9940\n",
      "Loss: 0.0199, Acc: 0.9940\n",
      "Loss: 0.0192, Acc: 0.9944\n",
      "Loss: 0.0184, Acc: 0.9947\n",
      "Loss: 0.0182, Acc: 0.9948\n",
      "Loss: 0.0184, Acc: 0.9945\n",
      "Loss: 0.0180, Acc: 0.9946\n",
      "Loss: 0.0183, Acc: 0.9944\n",
      "Loss: 0.0182, Acc: 0.9944\n",
      "Loss: 0.0182, Acc: 0.9944, Time: 2s\n",
      "Validation: Loss: 0.0270, Acc: 0.9904\n",
      "--------------------Epoch:3--------------------\n",
      "Loss: 0.0110, Acc: 0.9981\n",
      "Loss: 0.0140, Acc: 0.9959\n",
      "Loss: 0.0142, Acc: 0.9960\n",
      "Loss: 0.0130, Acc: 0.9964\n",
      "Loss: 0.0129, Acc: 0.9964\n",
      "Loss: 0.0129, Acc: 0.9966\n",
      "Loss: 0.0127, Acc: 0.9965\n",
      "Loss: 0.0125, Acc: 0.9966\n",
      "Loss: 0.0129, Acc: 0.9963\n",
      "Loss: 0.0126, Acc: 0.9963\n",
      "Loss: 0.0125, Acc: 0.9963\n",
      "Loss: 0.0125, Acc: 0.9963\n",
      "Loss: 0.0124, Acc: 0.9963\n",
      "Loss: 0.0122, Acc: 0.9963\n",
      "Loss: 0.0122, Acc: 0.9964, Time: 2s\n",
      "Validation: Loss: 0.0256, Acc: 0.9908\n",
      "--------------------Epoch:4--------------------\n",
      "Loss: 0.0051, Acc: 0.9994\n",
      "Loss: 0.0064, Acc: 0.9988\n",
      "Loss: 0.0063, Acc: 0.9985\n",
      "Loss: 0.0064, Acc: 0.9986\n",
      "Loss: 0.0068, Acc: 0.9982\n",
      "Loss: 0.0071, Acc: 0.9981\n",
      "Loss: 0.0077, Acc: 0.9979\n",
      "Loss: 0.0088, Acc: 0.9973\n",
      "Loss: 0.0093, Acc: 0.9971\n",
      "Loss: 0.0092, Acc: 0.9971\n",
      "Loss: 0.0091, Acc: 0.9972\n",
      "Loss: 0.0090, Acc: 0.9972\n",
      "Loss: 0.0094, Acc: 0.9970\n",
      "Loss: 0.0093, Acc: 0.9969\n",
      "Loss: 0.0095, Acc: 0.9968, Time: 2s\n",
      "Validation: Loss: 0.0193, Acc: 0.9936\n",
      "--------------------Epoch:5--------------------\n",
      "Loss: 0.0071, Acc: 0.9988\n",
      "Loss: 0.0078, Acc: 0.9978\n",
      "Loss: 0.0067, Acc: 0.9981\n",
      "Loss: 0.0080, Acc: 0.9978\n",
      "Loss: 0.0077, Acc: 0.9980\n",
      "Loss: 0.0072, Acc: 0.9980\n",
      "Loss: 0.0072, Acc: 0.9979\n",
      "Loss: 0.0069, Acc: 0.9980\n",
      "Loss: 0.0066, Acc: 0.9982\n",
      "Loss: 0.0066, Acc: 0.9982\n",
      "Loss: 0.0066, Acc: 0.9982\n",
      "Loss: 0.0071, Acc: 0.9978\n",
      "Loss: 0.0070, Acc: 0.9977\n",
      "Loss: 0.0068, Acc: 0.9978\n",
      "Loss: 0.0068, Acc: 0.9978, Time: 3s\n",
      "Validation: Loss: 0.0261, Acc: 0.9936\n",
      "Finish Training!\n"
     ]
    }
   ],
   "source": [
    "epoches = 5\n",
    "trainY = []\n",
    "trainX = []\n",
    "valY = []\n",
    "total_train = len(train_h5_loader.dataset)         # 总训练样本数\n",
    "total_val = len(val_h5_loader.dataset)             # 总验证样本数\n",
    "# print(total_number)\n",
    "for epoch in range(epoches):\n",
    "    print('{}Epoch:{}{}'.format('-'*20, epoch+1, '-'*20))\n",
    "    start = time.time()                             # 开始时间\n",
    "    train_loss = 0\n",
    "    train_acc = 0\n",
    "    for i,data in enumerate(train_h5_loader, 1):    # 后面参数1使i从1开始\n",
    "        feature, label = data                       # 读取数据\n",
    "        feature, label = map(lambda x:x.to(device), [feature, label]) \n",
    "#         print(feature.shape, label.shape)\n",
    "        classifier_model.to(device)\n",
    "    \n",
    "        out = classifier_model(feature)             # 前向传播 (N,1024)\n",
    "#         print(out.shape)\n",
    "        loss = criterion(out,label)\n",
    "        print_loss = loss.item()\n",
    "#         print(print_loss)\n",
    "        optimizer.zero_grad()                       # 梯度归零\n",
    "        loss.backward()                             # 反向传播\n",
    "        optimizer.step()                            # 更新参数\n",
    "        \n",
    "        train_loss += print_loss*label.shape[0]     # 累加一个批次的损失，损失函数默认取平均\n",
    "        _, pred = torch.max(out, 1)                 # 取最大的值为输出\n",
    "        num_correct = torch.sum(pred==label)        # 统计正确的数目\n",
    "        train_acc += num_correct.item()             # 注意使用item提取常数!!!\n",
    "        if i % 50 == 0:\n",
    "            current_loss = train_loss/(i*train_batch)\n",
    "            current_acc  = train_acc/(i*train_batch)\n",
    "            trainX.append(i+ total_train*epoch)\n",
    "            trainY.append(current_loss)\n",
    "            print('Loss: {:.4f}, Acc: {:.4f}'.format(current_loss, current_acc))\n",
    "    train_loss /= total_train\n",
    "    train_acc /= total_train\n",
    "    period_time = time.time() - start              # 统计一个epoch的耗时\n",
    "    print('Loss: {:.4f}, Acc: {:.4f}, Time: {:.0f}s'.format(\n",
    "        train_loss, train_acc, period_time))\n",
    "    \n",
    "    # 进行模型验证\n",
    "    print('Validation:', end=' ')\n",
    "    classifier_model.eval()\n",
    "    eval_loss = 0\n",
    "    eval_acc = 0\n",
    "    for data in val_h5_loader:\n",
    "        feature, label = data\n",
    "        feature, label = map(lambda x:x.to(device), [feature, label])\n",
    "        out = classifier_model(feature)\n",
    "        loss = criterion(out, label)\n",
    "        _, pred = torch.max(out, 1)\n",
    "        correct_num = torch.sum(pred==label)\n",
    "        eval_acc += correct_num.item()\n",
    "        eval_loss += loss.item() * label.shape[0]\n",
    "    current_val_loss = eval_loss/total_val\n",
    "    valY.append(current_val_loss)\n",
    "    print('Loss: {:.4f}, Acc: {:.4f}'.format(current_val_loss, eval_acc/total_val))\n",
    "print('Finish Training!')\n",
    "model_path = os.path.join('.', 'model')\n",
    "if not os.path.exists(model_path):\n",
    "    os.mkdir(model_path)\n",
    "save_path = os.path.join(model_path, 'fc2_model.pth')\n",
    "torch.save(classifier_model.state_dict(), save_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1d7058e0358>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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W4F5nHac2jh/UnZI2BT4V5dRK3isLH1g4Tu2UtElYc6/yKVsnLXmvLMCN8hynLkaVl7F0wzbmrNyca1GcZkreKwvfOus4dTNyaGTN7Y4FnXTkvbIAdyToOHXh1txOXeS9svBxhePEY1R5KVMWb2CNx+Z2UpD3ysJxnHhUx+Z+16einH1pFcrCF7gdp26G9+rMAZ1LfN3CSUneKwtf33aceEhiZHkpr851a25nX/JeWYAb5TlOXBLW3G+5NbdTg6wqC0mnS5ojaZ6ka1Ncv0rSDElTJb2WHPRe0nWh3BxJpzVCioYXdZw0xGjbxZLGhOvjJfUP6ceE9j5V0jRJ5yWVWZT0PkxMSt9f0guS5oa/KcOqZoLjBro1t5OarCmLEOnuTqLIX8OAS5KVQeBBMzvUzEYANwO3hbLDiPz/DwdOB/6QiJzXEHzNwskkMdv2FcB6MxsE3A7cFNLfASpCmz8duCsRDCnwMTMbYWYVSWnXAuPMbDAwLpxnhYQ19zi35nZqkM2RxTHAPDNbYGY7iGJsn5ucwcw2JZ12YM+M0bnAwyEW90JgHg0MJelrFk4WqLNth/P7wvGjwChJMrOtIYokQAnxZkmT67qPEKs+W7g1t5OKbCqL3sDipPMlIW0vJH1d0nyikcU19SnrODkiTvuszhOUw0ZCYCNJx0qaCcwgChmcUB4GPC9pkqQrk+oqM7Ploa7lQGkqoSRdKWmipImrV69u8MONcmtuJwXZVBap+vT79KLM7E4zGwj8APhRfcrGfzl8OO1klDjtM20eMxtvZsOBo4HrJJWE68eb2ZFE01tfl3RSfYQys7vNrMLMKnr06FGfontR2rmEw/p04UVft3CSyKayWAL0TTrvAyyrJf/D7Blexyob5+XwWSgnC8Rpn9V5wppEF6JY3NWY2WxgC3BIOF8W/q4CHmfP1OtKST1DXT2BrHf5Rw0tY6pbcztJZFNZTAAGSxogqS3RgvXY5AySBiedngXMDcdjgYvDjpIBwGDg7YYK4ut0Toaps22H88vC8fnAS2ZmoUwRgKQDgSHAIkkdJHUK6R2AU4kWw2vWdRnwf1l6rmpGlZdiBi+5NbcTKKo7S8Mws12SrgaeAwqBe81spqQbgIlmNha4WtJoYCewnvBChHyPALOAXcDXzaxBVkK+wO1kmpht+x7gfknziEYUF4fiJwDXStoJ7Aa+ZmZrJB0EPB68JBcR7RR8NpS5EXhE0hXAB8AF2X7G4b0607NLCeNmr+TCir51F3DynqwpCwAzexp4ukbaT5KOv1FL2V8Bv8qMHJmoxXH2EKNtV5LiR93M7gfuT5G+ADg8zb3WAqMaKXK9iGJzl/L4lKVU7qyipE2Dd647eULeW3DLVy0cp0GMKi9l644q3lqwNteiOM2AvFcW4PEsHKchJKy5fd3CgVagLCSfhnKchhBZc/dwa24HaA3KAreycJyGMrq8lKUbtvHuCrfmbu3kv7Lw7VCO02D2xOZ2A73WTt4rC/BpKMdpKKWdSzi8TxdedNcfrZ7WoSx8IspxGsyo8jKmLdnA6s1uzd2ayXtl4bNQjtM4EtbcHpu7dZP3ygLwFW7HaQTDekbW3O5YsHWT98pCcl3hOI0hYc392rw1VO702NytlfxXFm7B7TiNZnR5mVtzt3LyXlkAblDkOI3kowO70a5NoQdEasXkvbLwaSjHaTwlbQo5YXB3xs1e6Z2vVkr+KwvczsLJPJJOlzRH0jxJ16a4XixpTLg+XlL/kH6MpKnhM03SeSG9r6SXJc2WNFPSN5Lq+pmkpUnlzmyq50xmdHkpyzZWMnu5W3O3RvJfWfjeWSfDSCoE7iQKfzoMuETSsBrZrgDWm9kg4HbgppD+DlBhZiOA04G7QjCkXcB3zKwc+AhRWNXkOm83sxHhs5dr9KbiY27N3arJqrKI0fv6tqRZkqZLGhcihyWuVSX1pGpGIasXPrBwMswxwDwzW2BmO4hCAp9bI8+5wH3h+FFglCSZ2VYz2xXSS9gTl3u5mU0Ox5uB2UDvLD9HvSjtVMLhfbvyottbtEqypixi9r6mEPWyDiN6oW5OurYtqSd1ToPlwBe4nYzTG1icdL6EfX/Yq/ME5bAR6AYg6VhJM4EZwFVJyoNwvT9wBDA+Kfnq0Km6V9J+qYSSdKWkiZImrl69uqHPViujhpYybfEGVm2uzEr9TvMlmyOLOntfZvaymW0Np28RBb7PLD4L5WSeVK2qZo8kbR4zG29mw4GjgesklVQXkjoCjwHfNLNNIfmPwEBgBLAcuDWVUGZ2t5lVmFlFjx496vM8sRlVHk1FvfJudpSR03zJprKI0/tK5grgmaTzktBLekvSJ1MViNuT8nGFk2GWAMmBqfsAy9LlCWsSXYhicVdjZrOBLcAhIV8bIkXxgJn9MynfSjOrMrPdwJ+IOmI5YVjPzvRya+5WSTaVRZzeV5RR+hxQAdySlNzPzCqAzwB3SBq4T2UxelJKe1fHaTATgMGSBkhqC1wM1FxXGwtcFo7PB14yMwtligDCGt0QYJGinRj3ALPN7LbkiiT1TDo9j2iRPCdIYmR5Ka/OdWvu1kY2lUWc3heSRgPXA+eYWbVbSzNbFv4uAF4hmsOtN74bysk0YY3hauA5ooXoR8xspqQbJCXW1+4BukmaB3wbSGzwOAGYJmkq8DjwNTNbAxwPfB4YmWKL7M2SZkiaDnwM+FZTPGc6RpWXsW1nFW+6NXeroiiLdVf3voClRL2vzyRnkHQEcBdwupmtSkrfD9hqZtsldSd6kZIXv+uFuyh3Mk3Yvvp0jbSfJB1XAhekKHc/cH+K9NdIs8JmZp9vrLyZ5KMHdaN920LGzV7Jx4aU5locp4nI2sgiZu/rFqAj8I8aW2TLgYmSpgEvAzea2ayGyOFGeY6TWaLY3N15yWNztyqyObKI0/sanabcG8ChmZDBZ6EcJ/OMLi/j+VkrmbV8E8N7dcm1OE4TkPcW3OAjC8fJNKcMjTaUuGPB1kPeKwshX7NwnAyTsOYe59bcrYb8VxbykYXjZIPRbs3dqmgFykI+rnCcLDCqvAzw2NythfxXFrhvKMfJBuU9OwVrblcWrYH8VxY+DeU4WUESo8rLeM2tuVsFea8sCnwaynGyxqjy0siae75bc+c7ea8sJNjtQwvHyQofCdbc7lgw/8l/ZYFPQzlOtqi25n7XrbnznfxXFj4N5ThZZXR5Gcs3VjJr+aa6MzstllagLHw3lONkk48NLUVya+58J/+VBT4N5WSeGPHliyWNCdfHh1CpSDomyQX5NEnn1VVniIExXtLcUGfbpnjGuPToVMzhfboyztct8pq8VxbRbijXFk7miBlf/gpgvZkNAm4Hbgrp7xDFnR8BnA7cJamojjpvAm43s8HA+lB3s2J0eSnTlmxk1Sa35s5XYikLSQMlFYfjUyRdI6lrdkXLDBLs3p1rKZzmSgPbdp3x5cP5feH4UWCUJJnZ1uC+H6CEPXEcU9YZIuiNDHUQ6kwZZjiXJKy5X3Jr7rwl7sjiMaBK0iCiCGADgAfrKhRjqP5tSbMkTZc0LoSZTFy7LAy750q6rGbZuLgjQacOGtK248SXr84TlMNGoBuApGMlzQRmAFeF6+nq7AZsSFIwaWPZx41Jnw2GHtCJ3l3buTV3HhNXWewOjfU84A4z+xbQs7YCMYfqU4iG5IcR9ZxuDmX3B34KHEvU4/ppiJ5XbyI7i4aUdFoJ9W7bxIsvnzaPmY03s+HA0cB1kkpqyR87ln2cmPTZIrLmLuW1eavdmjtPiassdkq6hCgA/ZMhrU0dZeocqpvZy2a2NZy+RRSnG+A04AUzW2dm64EXiOZ3641EmlfLcYCGte048eWr80gqAroA65IzmNlsYAtwSC11rgG6hjrS3atZMHJoKZU7d7s1d54SV1lcDnwU+JWZLQxxtf9eR5k4Q/VkrgCeqU/ZOMNuX+B26qAhbbs6vnzYmXQxMLZGnrFECgjgfOAlM7NQpgggTLsOARalq9Oifd8vhzoIdf5fwx83e7g1d34TK6xqiH99DUCYDupkZjfWUSz28FnS54AK4OT6lDWzu4G7ASoqKtLU7dNQTnoa0rbNbJekRHz5QuDeRHx5YKKZjSVa/7hf0jyiEcXFofgJwLWSdgK7ga+Z2Zpw/33qDGV+ADws6ZdEU7f3ZOjxM0pJm0JOHLzHmlse0ziviKUsJL0CnBPyTwVWS/q3mX27lmJxhupIGg1cD5xsZtuTyp5So+wrcWStSYHkRnlOWhrYtuPEl68ELkhR7n7g/rh1hvQFRNO6zZ5R5WU8N3MlM5dt4pDeHps7n4g7DdXFzDYBnwL+YmZHAaPrKFPnUF3SEcBdwDlmlryN4jngVEn7hd7eqSGtQfjIwqmFhrRtJw0j3Zo7b4mrLIok9QQuZM8iYK2EHSaJYfVs4JHEUF3SOSHbLUBH4B/BonVsKLsO+AWRwpkA3BDS6o2PLJw6qHfbdtLTvWMxI/p2Zdy7vm6Rb8SahgJuIPrRf93MJkg6CJhbV6EYQ/W0PTgzuxe4N6Z8aSmQb4ZyaqVBbdtJz+jyMm55bg4rN1VS1rkk1+I4GSLWyMLM/mFmh5nZV8P5AjP7dHZFywySPJ6Fk5aW3LabK6PKSwG35s434rr76CPpcUmrJK2U9JikPnWXzD0FHlbVqYWW3LabK0PKImtuX7fIL+KuWfyFaHG6F5G9wxMhrQUgX+B2aqMFt+3miVtz5ydxlUUPM/uLme0Kn78CTetPoIEUeDwLp3ZabNtuzowqL6Ny527emL8m16I4GSKuslgj6XOSCsPnc0CLsOmPdkPlWgqnGdNi23Zz5iMH7U+HtoXuWDCPiKssvki0tXAFsJzI9cDl2RIqkxQU4AvcTm202LbdnCkuKuTEwT14abbH5s4X4u6G+sDMzjGzHmZWamafJDJiavb4biinNlpy227ujCovZcWmSmYu89jc+UBjIuXV6g6huVAgX+B26k2LaNvNnURsbncsmB80Rlm0CC9hBfJpKKfetIi23dzp3rGYI/p29S20eUJjlEWL+AUu9Gkop/54g8kQo8rLmLF0Iys9NneLp1ZlIWmzpE0pPpuJ9qU3eyR5DG4nFUc0tG3HCBdcLGlMuD5eUv+Q/nFJkyTNCH9HhvROwTda4rNG0h3h2hckrU669qWMfxNZxK2584dafUOZWaemEiRbuJ2Fk4YpZlZR30JJ4YI/TuRKf4KksSEuRoIrgPVmNkjSxcBNwEVEUe/ONrNlkg4h8knV28w2AyOS7jEJ+GdSfWPM7Or6ytoc2GPNvZJLjumXa3GcRtCYaagWQYFElSsLJ3PUGS44nN8Xjh8FRkmSmU0xs0RMl5lAiaTi5IKSBgOlwKtZe4ImRBKjy0t5bd4at+Zu4eS/sigQVT4N5WSOOCF/q/MEV/0bgW418nyaaHSzvUb6JUQjieQezqclTZf0qKS+pCFOmOFckLDmfn2eW3O3ZLKqLGLM7Z4kabKkXZLOr3GtKmmetmZ849gUulGek1nihPytNY+k4URTU19Jke9i4KGk8yeA/mZ2GPAie0Ys+97A7G4zqzCzih49mo/HkmPdmjsvyJqySJrbPQMYBlwiaViNbB8AXwAeTFHFNjMbET7npLgei0KJKje0cDJHnHDB1XkkFQFdiOJwEzzaPg5cambzkwtJOhwoMrNJiTQzW5s0+vgTcFTmHqVpKC4q5KSDe/DSuyt9/bAFk82RRZ1zu2a2yMymEwWuzwpuwe1kmDrDBYfzy8Lx+cBLZmaSugJPAdeZ2esp6r6EvUcVhCh+Cc4hijrZ4hhVXsbKTdt5Z6lbc7dUsqks4szt1kZJmH99S9InGypEYYHY7SMLJ0PEDBd8D9BN0jwia/DEFOzVwCDgx0lTrKVJ1V9IDWUBXCNppqRpwDVEI/EWx8eG9ECCa/85nX9MXMzWHbtyLZJTT+KGVW0IceZ2a6Nf2GJ4EPCSpBkphu1XAlcC9OuXelteYYHvhnIyS4xwwZXABSnK/RL4ZS31HpQi7TrgusbI2xzo1rGYmz99GH94ZT7fe3Q6P39iFmcf3pMLK/oyom9XJDeab+5kU1nEmdtNS2KLoZktkPQKcAQwv0aeu4G7ASoqKlJqhGhkUS+5HcfJAhdvTqmcAAAez0lEQVRU9OX8o/owYdF6xkxYzL+mLOOhtxdzcFlHLqzoy6eO7MP+HdrmWkwnDdmchoozt5sSSfsl9p9L6g4cD8yqvVRqCiV2ubZwnGaBJI4ZsD+3Xng4b18/il+fdyjt2hbxy6dmc+yvX+RrD0zi3++t9k0pzZCsjSzMbJekxNxuIXBvYm4XmGhmYyUdTbQzZD/gbEk/N7PhQDlwl6TdRArtxhoWsrEpLIi8zpqZD3UdpxnRqaQNnzm2H585th9zVmxmzITFPD5lCU/PWEGvLiWcX9GXC47qQ9/92+daVIfsTkPFmdudQDQ9VbPcG8ChmZChsCBSEFW7jaJCVxaO0xwZckAnfnL2MH5wxhBenLWKMRMX898vzeW/X5rL8QO7c+HRfTl1WBklbQpzLWqrJavKojmQUBa7dhtF3s4cp1lTXFTIWYf15KzDerJ0wzb+MXEx/5i4hGsemkKXdm0474jeXFjRl2G9Ouda1BZFJmZW8l5ZFAVl4bYWjtOy6N21Hd8cfTDXjBzM6/PXMGbCYh4c/wF/fWMRh/buwoVH9+Wcw3vRpV2bXIva7Pn6g5MZ2KMj3zl1SIPryHtlkTyycByn5VFQIE4c3IMTB/dg/ZYd/GvqUsZMWMyP//UOv3xyFmceGm3B/chB+/u6ZAomvb+Op2es4HunNVxRQCtQFomRRVWVKwvHaens16Etlx8/gC8c158ZSzcyZsJixk5dxuNTlnJgt/ZcGLbnlnUuybWozQIz4+Zn59C9YzGXH9+/UXXlvbIoLIx2B/vIwnHyB0kc1qcrh/Xpyo/OGsYz7yxnzITF3PLcHG59fg6nDCnlwoq+jCovpU1h3jvXTst/5q5h/MJ1/Pyc4bRv27if+7xXFkXV01Bua+E4+Ui7toV86sg+fOrIPixas4VHJi7m0UlLeOndVXTv2JZPHdmHCyv6Mqi0Y65FbVJ27zZuee5dendtx8XHpPVsH5u8V7nVysKnoRwn7+nfvQPfP30ob1w7kj9fWsER/fbjntcWMvq2f3PhXW+yanPriQX+7MwVvLN0E9/6+MEUZ2AraP6PLAp9gdtxWhtFhQWMHlbG6GFlrNpcyT8nL+W259/jpmfmcOuFh+davKyzq2o3v31+DoNLO3LeEfXx35qeVjCyCGsWHi7PcVolpZ1KuOrkgXzxhAE8NnkJUxdvyLVIWeefk5eyYPUWvnPqkOodoY0l75VFmzCy2OnTUE4GiREFsljSmHB9vKT+If3jkiZJmhH+jkwq80qocy/35enqcurH1SMH0b1jMTc8MTOvgzBV7qzijhff4/C+XTlteFnG6m0FyiKxG8pHFk5miBkF8gpgvZkNAm4nCqMKsAY428wOJQqQdH+Ncp9NihC5qo66nHrQsbiI758+hMkfbGDstNgOsFscD4z/gGUbK/n+aUMyaneS98qiKCgLH1k4GaTOKJDhPBEv+1FglCSZ2ZSE+31gJlGQr+I67peyrkY/RSvk/CP7cGjvLvzm6XfzMgDTh9t3cefL8zh+UDeOH9Q9o3XnvbJITEPt2OUjCydjxIkCWZ0nRNfbCHSrkefTwJSkGNsAfwlTUD9OUghx6kLSlSG65MTVq1c37MnynIIC8ZOzh7FiUyX/++8FuRYn49zz6kLWbdnB904bmvG6815ZJLaM7fAFbidzxIkCWWseScOJppO+knT9s2F66sTw+Xw97oeZ3W1mFWZW0aNHj1rEb90c3X9/zj68F3f9ez5LN2zLtTgZY/2WHfzp1QWcOqyMEX27Zrz+VqAsokfcvrMqx5I4eUScKJDVeSQVAV2AdeG8D1Ecl0uTQwWb2dLwdzPwINF0V611OQ3j2jOGIsGNz7yba1Eyxh//PZ8tO3bx3Ub6gEpHVpVFjB0jJ0maLGmXpPNrXLtM0tzwuayhMrQNysJHFk4GiRMFcizRAjbA+cBLZmaSugJPAdeZ2euJzJKKQlRIJLUBPgG8U1tdWXiuVkPvru248qSBPDFtGRMWtXy9u2JjJfe9sYjzjujNwWWdsnKPrCmLmDtGPgC+QNSLSi67P/BT4Fii3tVPJe3XEDnaVi9wu7JwMkNYN0hEgZwNPJKIAinpnJDtHqCbpHnAt4FEZ+lqYBDw4xpbZIuB5yRNB6YCS4E/1VGX0wiuOvkgDuhcwg1PzGJ3Czfa/d24uew241ujD87aPbJpwV29YwRAUmLHSHV4VDNbFK7V/CU/DXjBzBLD9heA04GH6itE2+ppKFcWTuaIEQWyErggRblfAr9MU+1Rae6Vsi6ncbRvW8R1Zw7lGw9P5dHJS7iwovH+k3LBwuAP63PH9stqCNpsTkPF2THSqLJxdn8k1iwqfc3CcZwanHN4L47s15Wbn53D5sqduRanQdz2wnu0LSzg6pGDs3qfbCqLWDs4GlM2zu6PTiVt6Nq+DQ++/QEbt7bMxuA4TnaQxE/PHs6aD7dz58vz6y7QzJi5bCNPTFvGF0/oT49OdZnrNI5sKos4O0ayUXYv2hYV8IfPHsnCNVv48v0TfYThOM5eHN63K586sjf3vraQ99duybU49eLW59+jc0kRV540MOv3yqayiLNjJB3PAadK2i8sbJ8a0hrEcQO7c+uFI3h74Tq+88i0Fr+Y5ThOZvnB6UMpKhS/fnp2rkWJzcRF63jp3VVcdcrAJolDnjVlEWfHiKSjJS0hWry7S9LMUHYd8AsihTMBuCGx2N1Qzjm8F9efWc5TM5bzi6dm5bUjMcdx6kdZ5xK+/rFBPDdzJW/MW5NrceokES61R6diLj9uQJPcM6vxLGLsGJlANMWUquy9wL2ZlOdLJw5g+cZK7n19Ib26tOPLJx2Uyeodx2nBXHHCAB56+wNueHIWT/7XCdV+5Zojr7y3mrcXreMX5w6nXdvGBzaKQ/P9NrKAJH50VjlnHdqTXz09O689TzqOUz9K2hRy/ZnlvLtiMw9PWFx3gRyxe7dxy7Nz6Lt/Oy46ul+T3bdVKQuIHIndeuHhHDNgf77zyFTemN/8h5yO4zQNpx9yAMcO2J9bn5/TbHdPPjVjObOWb+LbHz+42o6sKWh1ygKiHsSfPl/BgO4d+MrfJvHuik25FslxnGaAFHml3bBtJ78bNzfX4uzDzqrd3PbCewwp68Q5h2cmXGpcWqWyAOjSvg1/vfwYOhQX8YV7J7Asj7xPOo7TcIb36sLFR/flb28uYt6qD3Mtzl48NmkJC9ds4bunZS5calxarbIA6NW1HX/94tFs2b6LL/zl7WY77HQcp2n5zqlDaNemkF89NavuzE1E5c4qfjduLkf068ro8tImv3+rVhYAQw/ozF2XHuVGe47jVNO9YzHXjBrMy3NW8/KcVXUXaAL+/tb7LN9YyfcyHC41Lq1eWYAb7TmOsy+XHdefAd078MsnZ+Xca/Xmyp3c+fI8ThzcneMGZjZcalxcWQTcaM+JS4w4LcWSxoTr4yX1D+kflzRJ0ozwd2RIby/pKUnvSpop6cakur4gaXWSO/MvNdVztnbaFhXwo7PKmb96C/e/+X5OZfnzqwtZv3Un38tSYKM4uLJI4ksnDuDy4/vzl9cX8edXF+ZaHKcZEjNOyxXAejMbBNxOFD4VYA1wdgidehlwf1KZ35rZUOAI4HhJZyRdG2NmI8Lnz5l/KicdI4eWcuLg7tzx4nus27IjJzKs/XA7f351AWcccgCH9cl8uNS4uLJIQhI/PmuYG+05tVEdp8XMdgCJOC3JnAvcF44fBUZJkplNMbNEo5oJlEgqNrOtZvYyQKhzMmk8GzhNiyR+8olhbNlRxW0vzMmJDH94ZT7bdlbxnVOzF9goDq4sauBGe04dxIm1Up0n+EjbCHSrkefTwBQz256cGMKung2MS84rabqkRyWljdATJ76LU38Gl3Xic8f248HxHzS5TdayDdu4/633+fSRfRhUmp1wqXFxZZGChNFe/25utOfsQ5xYK7XmkTScaGrqK3sVkoqIokH+PhFhEngC6G9mhwEvsmfEsu8NYsR3cRrGtz5+MJ3bteGGJ5p2PfP34+aCwTdGZzewURxcWaShS/s23PdFN9pz9iFOrJXqPEEBdAESIYL7AI8Dl5pZzWg7dwNzzeyORIKZrU0affyJNKFXnezStX1bvjX6YN6Yv5bnZ61sknsuWP0h/5i0hM8c248++2UvXGpcXFnUghvtOSmIE6dlLNECNsD5wEtmZmGK6SngOjN7PbmApF8SKZVv1kjvmXR6DpG7fycHfPbYfgwu7civn57N9l3Zt8e69YX3KC4q4OqRg7J+rzhkVVk0Yothf0nbkrYL/m825awNN9pzkokTpwW4B+gmaR7wbSDR9q8GBgE/TmrbpWG0cT3R7qrJNbbIXhO2004DrgG+0BTP6exLUWEBP/7EMN5fu5W/vL4oq/d6Z+lGnpq+nCtOGED3jtkNlxoXZWv+LWwxfA/4ONGwfAJwiZnNSsrzNeAwM7tK0sXAeWZ2UVAaT5rZIXHvV1FRYRMnTszkI+zF2GnLuOahKZx1aE/++5IjKGhivyxOZpE0ycwqci1Htsj2+9Ca+dJ9E3hz/lpe/t4plHYqyco9Lrv3baYt2cB/vv8xOpdkNwpe3HchmyOLBm8xzKJMDcaN9hzHAbj+rGHsqNrNb5/Lzlba8QvW8u/3VvPVkwdmXVHUh2wqi8ZuMRwgaYqkf0s6MdUNmnqroBvtOY4zoHsHvnBcf/4xaQnvLN2Y0brNjFuem0NZ52IuO65/RutuLNlUFo3ZYrgc6GdmRxDN+T4oqfM+GZt4q6Ab7TmOA/Bfowazf/u2/PyJmRmdZXh5ziomvr+ea0YNpqRN04RLjUs2lUWDtxia2XYzWwtgZpOA+UBuzRcDbrTnOE7nkjZ897QhTFi0nienL89Inbt3G7c89x4HdmvPhRVpbS9zRjaVRWO2GPYIC+RIOggYDCygmeBGe47jXFjRl/KenbnxmXczskvyienLmB3CpbYpbH5WDVmTqJFbDE8Cpoftgo8CV5nZumzJ2hASRnvtiwu57N63WepGe47TqigsED89exhLN2zj7v80ri+bCJc69IBOnH1YrwxJmFmyqr7M7GkzO9jMBprZr0LaT8xsbDiuNLMLzGyQmR2TcHFgZo+Z2XAzO9zMjjSzJ7IpZ0Pp1bUdf738GLZur+IL97rRnuO0Nj5yUDfOPPQA/vjKfJZvbHiH8ZGJi3l/7Va+d9qQZrstv/mNdVoY5T0jo71Fa91oz3FaI9edUU6VGTc9826DylfurOL34+Zy1IH7MXJo04dLjYsriwxw3MDu/PaCwz3SnuO0Qvru354vnziAf01dxuQP1te7/N/eXMTKTdv5fo7CpcbFlUWGOHdEbzfac5xWytdOGURpp2J+/sSsenUWN1Xu5A+vzOfkg3tw7EE1vdg3L1xZZBA32nOc1kmH4iJ+cPpQpi3ewONTlsYu9+f/LGBDjsOlxsWVRQZxoz3Hab2cd0RvDu/blZuefZct23fVmX/Nh9v582sLOevQnhzSu0sTSNg4XFlkGDfaax00wqPyxyVNkjQj/B2ZVOaokD5P0u8TftIk7S/pBUlzw9/9muo5nfgUFEQhWFdt3s4fX6kZqmRf7nx5Htt37ebbOQ6XGhdXFlkg2Wjv6w9MpsoXvPOKYDB6J3AGkVvxSyQNq5HtCmC9mQ0CbieKjAewBjjbzA4lMki9P6nMH4EriYxQBwOnh/RrgXFmNpgo3Oo+yslpHhx14H58ckQv7n51AYvXbU2bb8n6rTzw1gecf2QfBvbo2IQSNhxXFlmiS/s2nHRwD9Zv3ZlxZ2NOzmmwR2Uzm2JmifnJmUBJGIX0BDqb2ZsW7Y74G/DJFHXdl5TuNEN+cMZQCiV+80z6OFW/e3EuqHmES41LUa4FyGcSG6LOvfN1Lv3ogfTs0o6eXUrCpx1lXYopLmpezsKcWKTyqHxsujxmtktSwqNy8rzkp4EpZrZdUu9QT3KdCS/NZWa2PNS1XFLz3Yzv0LNLO646eSC3v/geby1Yy0dq7HKat+pDHpu8hMuPH0Cvru1yJGX9cWWRRX545lCefWc5yzZW8viUpWyu3HfRq3vHthwQlEfPpL8HdCmhlyuU5kpjPCpHF6XhRFNTp9ajztqFkq4kmsaiX79+9SnqZJgrTzqIMRM+4IYnZvHEf51AYZJV9m0vzKFdm0K+dsrAHEpYf1xZZJGiwgLeuG5U9fmH23exYmMlyzduY/nGSpZvqGTFpuj4g7VbGb9gLZtiKJSEIqlLodzwxCxKOxdz1cktq1G2AOrjUXlJskdlgBBG9XHgUjObn5S/T5o6V0rqGUYVPYFVqYQys7uBuyGKlNfAZ3MyQLu2hVx3Zjn/9dAUHpm4mEuOiZT3jCUbeXrGCr4xajDdmkm41Li4smhCOhYXMai0I4NK0y9obdm+i+UbK1mxsZJlG7ftpVwWr0uvULp1aEvPriUc0LkdvbpGCuXe1yNbj0cmLqZ314Si2aNwDugcTYl1ademWVuONkOqPSoDS4k8Kn+mRp6ER+U32dujclfgKeA6M3s9kTkogs2SPgKMBy4F/rtGXTeGv/+XtSdzMsYnDuvJ395cxG+fm8NZh/Wkc0kbbn7uXfZr34YvnTgg1+LVG1cWzYwO9VQo1aOUjZWs2LiNJeu3MmHROjZu2+PU8ODSTizfVMl7K1ezavN2ahqXl7QpiNZQOhfTM4xYegZlckBQLN07FNfbwdmtz89hc+UuOrdrQ0mbAoqLCilpU0BJUSHF4W9Jm+Tjgui8qIDiNtF528KCZqfIwhpEwqNyIXBvwqMyMDE4yrwHuD94VF5HpFAg8sQ8CPixpB+HtFPNbBXwVeCvQDvgmfCBSEk8IukK4APggmw/o9N4JPGTTwznnDtf439emsfHhpTy6tw1XH9mOZ2aUbjUuChf3FJ4gPq92bJ9F2s/3EGf/drt9SO/s2o3qzdvZ8WmhLKJlExC+azYVMnKTZXsrNq7XRQViLIk5dEz+TiMWEo7FVf74d+wdQcjbnih0c8hQXHRHiVS0qawWrEUJ5RO8rWQfs9r0ahq5s9Po0Pxvn2iuEHqWyr+PjQfvv/oNB6fspT+3TqwuXIXr3zvlGYVBS/uu5DVkYWk04HfEfW+/mxmN9a4Xky0RfAoYC1wkZktCteuI9qrXgVcY2bPZVPWfKNDcVHKH8k2hQX06tqu1l0Yu3cba7fsqB65rNxUmTSSqWTWsk2Mm72Syp279yonQY+OxRzQpYTpS6Ltwg9++Vg+MqAbO6p2s33nbip3VVG5s4rtu3ZTubOKyp27a5zvOa75t2a57buq2Fy5i9Wbt7N9126276yictdutu3Y4/n3wfEf8OWTDsrQt+o49ee7pw3h6RkrmLvqQ37zqUOblaKoD1lTFkmGSx8nWrybIGmsmc1KylZtuCTpYqLdIRcFA6eLgeFAL+BFSQebmfv/bgIKCkSPTsX06FTMoX1SuyEwMzZt28XyTUmjkoRC2VTJoNKObNq2k+MGdgegpCCacupC0wy/t+2o4vlZKzjn8OYZSMZpPZR2KuFn5wzn2XdWcP5Rfeou0EzJ5sii2nAJQFLCcClZWZwL/CwcPwr8T3BxcC7wsJltBxaGed9jiBYLnWaAJLq0b0OX9m0YekDnXIuzD+3aFnLuiN51Z3ScJuD8o/q0aEUB2bXgTmW4VPPt3ctwCUgYLsUp6ziO4zQR2VQWjTFcimWgJOlKSRMlTVy9enUDRHQcx3HikE1lUR/DJWoYLsUpi5ndbWYVZlbRo0ePDIruOI7jJJNNZVFtuCSpLdGC9dgaeRLGRpBkuBTSLw4O1gYQeeB8O4uyOo7jOLWQtQXuxhguhXyPEC2G7wK+7juhHMdxckdW7SzM7Gng6RppP0k6riSNNaqZ/Qr4VTblcxzHceLh8Swcx3GcOskbdx+SVgPvp7ncnb3jCOQSlyU1TS3LgWaWt7siankfmtP/PBe05udP9+yx3oW8URa1IWlic/ED5LKkpjnJks+09u+5NT9/Y5/dp6Ecx3GcOnFl4TiO49RJa1EWd+dagCRcltQ0J1nymdb+Pbfm52/Us7eKNQvHcRyncbSWkYXjOI7TCPJaWUg6XdIcSfMkXZvBevtKelnSbEkzJX0jpP9M0lJJU8PnzKQy1wU55kg6rS4Zg5uU8ZLmShoTXKakk2eRpBnhnhND2v6SXgjlX5C0X0iXpN+H+02XdGRSPZeF/HMlXZaUflSof14omzLOqaQhSc8+VdImSd/M1ffi7E223ofmTrr3tTUhqVDSFElPNrgSM8vLD5GLkfnAQUBbYBowLEN19wSODMedgPeAYUSxOb6bIv+wcP9iYECQq7A2GYFHgIvD8f8CX61FnkVA9xppNwPXhuNrgZvC8ZlEsZ0FfAQYH9L3BxaEv/uF4/3CtbeBj4YyzwBnxPz+VwAH5up78U/TvA/N/ZPufc21XE38HXwbeBB4sqF15PPIojr4kpntABLBlxqNmS03s8nheDMwm9rjbVQHczKzhUAimFNKGUPPfSRRQCiA+4BP1lPMc0O5muXPBf5mEW8BXSX1BE4DXjCzdWa2HngBOD1c62xmb1rU6v4WU5ZRwHwzS2comZClqb+X1krW3ofmTgPe17xCUh/gLODPjaknn5VFkwRQktQfOAIYH5KuDtM79yamfmqRJV16N2CDRQGh4shuwPOSJkm6MqSVmdlyiF4WoLSBsvQOxzXT6+Ji4KGk81x8L84ePKAYKd/X1sAdwPeB3Y2pJJ+VRawASo26gdQReAz4ppltAv4IDARGAMuBW+uQpVHBn5I43syOBM4Avi7ppNrEzrIshHWEc4B/hKRcfS/OHlr9d5fifc17JH0CWGVmkxpbVz4ri1gBlBqKpDZEDe8BM/sngJmtNLMqM9sN/Ilo6F+bLOnS1xBNDxXVSE+JmS0Lf1cBj4f7rgxTSIS/qxooy5JwXDO9Ns4AJpvZyiBXTr4XZy+y+j40d1K9r62E44FzJC0imnocKenvDaop1wsvWVzQKSJapB3AngW94RmqW0Rz93fUSO+ZdPwtovl4gOHsvZC7gGjBMa2MRL3y5IXcr6WRpQPQKen4DeB04Bb2XuC+ORyfxd4L3G+H9P2BhUSL2/uF4/3DtQkhb2KB+8w6vp+Hgctz+b34p+neh+b+Sfe+trYPcAqNWODO+QNk+cs5k2jnw3zg+gzWewLREH46MDV8zgTuB2aE9LE1fiSvD3LMIWk3UToZiXatvE206PsPoDiNLAeFF38aMDNRB9H8/jhgbvib+OEXcGe43wygIqmuL4b7zavxY18BvBPK/A/BmDONPO2BtUCXpLQm/17803TvQ3P/pHtfcy1XDr6HRikLt+B2HMdx6iSf1ywcx3GcDOHKwnEcx6kTVxaO4zhOnbiycBzHcerElYXjOI5TJ64smhmSPgx/+0v6TIbr/mGN8zcyWb/jZBNJVTW8GmfSk3R/Se9kqr58xLfONjMkfWhmHSWdQuSp9RP1KFtoZlV11Z0JOR2nqclm+w0+o540s0OyUX8+4COL5suNwImhB/Wt4I/+FkkTgkO+rwBIOiX46n+QyPANSf8KTgVnJhwLSroRaBfqeyCkJUYxCnW/E+JWXJRU9yuSHpX0rqQH0sWycJxcoSiey02S3g6fQSH9QEnjwvsyTlK/kF4m6XFJ08LnuFBVoaQ/hffmeUntQv5rJM0K9Tyco8fMPbm2KvTPPlaWH6aytgSuBH4UjouBiUSuG04BtgADkvImrLXbEVled0uuO8W9Pk3kkrwQKAM+IIoBcAqwkciPUAHwJnBCrr8j/7TOD1DFHgvsqcBFIX0RezwXXJp4b4AngMvC8ReBf4XjMUTOBAltvgvQH9gFjAjpjwCfC8fLCJ4CgK65/h5y9Uk4ZHOaP6cCh0k6P5x3AQYDO4j8Oy1MynuNpPPCcd+Qb20tdZ8APGTRFNZKSf8GjgY2hbqXAEiaSvRSvZaZR3KcerHNzEakufZQ0t/bw/FHgU+F4/uJAoJBFBPlUoDQ5jcGt/kLzWxqyDOJqK1D5CbkAUn/Av6Vgedokfg0VMtBwH+Z2YjwGWBmz4drW6ozRWsdo4GPmtnhwBSgJEbd6diedFwF3sFwmiWW5jhdnlSka+tnEflTOwqYlOT1uFXhyqL5spkoBGSC54CvBlfLSDpYUocU5boA681sq6ShRN5iE+xMlK/Bf4CLwrpID+AkImd9jtNSuCjp75vh+A2iIFwAn2XPiHgc8FWojk3dOV2lkgqAvmb2MlEAoa5Aq9wk0io1ZAthOrBL0jTgr8DviIbFk8Mi82pShxR9FrhK0nQiT65vJV27G5guabKZfTYp/XGiIfs0ot7X981sRVA2jtNcaBemQhM8a2aJ7bPFksYTdYAvCWnXAPdK+h7R+3J5SP8GcLekK4hGEF8lCsqVikLg75K6EI3AbzezDRl7ohaEb511HKdFEwL7VJjZmlzLks/4NJTjOI5TJz6ycBzHcerERxaO4zhOnbiycBzHcerElYXjOI5TJ64sHMdxnDpxZeE4juPUiSsLx3Ecp07+H2vPHFOvyalXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d74741fa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6,3.5))\n",
    "fig.tight_layout()#调整整体空白\n",
    "plt.subplots_adjust(wspace =0.4, hspace =0)  #调整子图间距\n",
    "plt.subplot(121)\n",
    "plt.title('Train loss curve')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Loss')\n",
    "plt.plot(trainX, trainY)        # epoch很小，画出的图很怪\n",
    "plt.subplot(122)\n",
    "plt.title('Val loss curve')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.plot(valY)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. 评估模型性能"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1 读取测试数据\n",
    "测试数据与训练和验证数据不同，没有給标签，所以需重新定义类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "..\\data\\dog_vs_cat\\kaggle_test\n"
     ]
    }
   ],
   "source": [
    "import imageio\n",
    "from PIL import Image\n",
    "test_path = os.path.join(root_path, 'kaggle_test')\n",
    "print(test_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义数据预处理方法\n",
    "img_transform = transforms.Compose([\n",
    "    transforms.Resize(250),                             # 缩放到250*250\n",
    "    transforms.CenterCrop(224),                         # 裁剪中心224*224尺寸的图像\n",
    "    transforms.ToTensor(),                              # 转换为[0,1]的向量\n",
    "    transforms.Normalize((0.5,0.5,0.5), (0.5,0.5,0.5))  # 转换为[-1,1]，数据零均值处理\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TestDataset(Dataset):\n",
    "    \"\"\"\n",
    "    在初始化时只保存文件名称，getitem时再读入图像，更节省时间\n",
    "    \"\"\"\n",
    "    def __init__(self, data_path, img_trans):\n",
    "        \"\"\"\n",
    "        data_path:测试数据的路径\n",
    "        img_trans:图像的预处理方法\n",
    "        \"\"\"\n",
    "        self.data_path = data_path                      # 记录路径名称\n",
    "        self.file_list = os.listdir(data_path)          # 记录名称列表\n",
    "        self.img_trans = img_trans                      # 数据预处理函数\n",
    "    def __len__(self):                            \n",
    "        return len(self.file_list)                            # 返回样本数量\n",
    "    def __getitem__(self, index):\n",
    "        assert index < len(self), 'Index is out of range'      # 保证索引不溢出\n",
    "#         print(len(self))\n",
    "        file_name = self.file_list[index]\n",
    "        file_path = os.path.join(self.data_path, file_name)\n",
    "        with Image.open(file_path) as img:\n",
    "            # 将PIL图像转换为Tensor并进行预处理, (C,H,W)\n",
    "            img = img.convert('RGB')\n",
    "            data = self.img_trans(img)                           \n",
    "        label = torch.ones([1])             # 注意原测试图像数据中没有给标签，这里使用1\n",
    "\n",
    "        return (data, label)                                   # 返回图像和标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3, 224, 224]) torch.Size([1])\n"
     ]
    }
   ],
   "source": [
    "test_datasets = TestDataset(test_path, img_transform)\n",
    "x, y = test_datasets.__getitem__(7)\n",
    "print(x.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_batch = 10\n",
    "test_datasets = TestDataset(test_path, img_transform)\n",
    "test_loader = DataLoader(test_datasets, batch_size=test_batch, shuffle=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([10, 3, 224, 224])\n"
     ]
    }
   ],
   "source": [
    "for test in test_loader:\n",
    "    x,y = test\n",
    "    print(x.shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2 测试模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\torchvision\\models\\squeezenet.py:94: UserWarning: nn.init.kaiming_uniform is now deprecated in favor of nn.init.kaiming_uniform_.\n",
      "  init.kaiming_uniform(m.weight.data)\n",
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\torchvision\\models\\squeezenet.py:92: UserWarning: nn.init.normal is now deprecated in favor of nn.init.normal_.\n",
      "  init.normal(m.weight.data, mean=0.0, std=0.01)\n"
     ]
    }
   ],
   "source": [
    "# 提取特征的模型，已经载入权重\n",
    "test_resnet = feature_net('resnet18')\n",
    "test_squeezenet = feature_net('squeeze')\n",
    "# 创建模型并载入权重\n",
    "test_classifier = classifier(1024, 2)    # 创建分类器模型,1024是特征的维度\n",
    "weight_path = os.path.join('./model', 'fc2_model.pth')\n",
    "test_classifier.load_state_dict(torch.load(weight_path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测结果：\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d71dc22358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_dict = { 0:'cat',  1:'dog'}\n",
    "plt.subplots_adjust(wspace =0, hspace =0)\n",
    "for data in test_loader:\n",
    "    with torch.no_grad():\n",
    "        img, lable = data                 # 读取测试数据\n",
    "#         print(img.shape)                  # [10, 3, 224, 224]\n",
    "        resnet_out = test_resnet(img)\n",
    "        squeeze_out = test_squeezenet(img)\n",
    "#         print(resnet_out.shape, squeeze_out.shape)\n",
    "        feature_cat = torch.cat([resnet_out, squeeze_out], 1)       # (N,1024) 拼接特征\n",
    "        classifier_out = test_classifier(feature_cat)               # (N, 2)\n",
    "#         print(classifier_out.shape)\n",
    "        _, pred = torch.max(classifier_out, 1)                      # (N,)\n",
    "        pred = pred.numpy()\n",
    "        print('预测结果：')\n",
    "        for i in range(10):\n",
    "            plt.subplot(2,5,i+1)\n",
    "            img_show = img[i]           \n",
    "            img_show = (img_show-img_show.min())/(img_show.max()-img_show.min())  # *255\n",
    "#             print(img_show.shape)\n",
    "            img_show =img_show.permute(1,2,0)                       # (H,W,C)\n",
    "            plt.title(class_dict[pred[i]])\n",
    "            plt.axis('off')\n",
    "            plt.imshow(img_show)\n",
    "        plt.show()\n",
    "    break                                                           # 只测试一个批次"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. `torch.FloatTensor()`零维度的向量可以直接与其他维度的向量进行torch.cat拼接\n",
    "2. CrossEntropyLoss对预测输出的大小没有要求，但标签必须是在[0,class-1]内的数字，并且是LongTensor；\n",
    "BCELoss损失函数，预测输出必须在(0,1)内，并且目标是FloatTensor，且维度与预测输出维度一致\n",
    "3. plt.subplots_adjust(wspace =0.4, hspace =0)调整子图的间距\n",
    "4. torch.stack堆叠的数据维度必须相同\n",
    "5. torch.unsqueeze(img_t, 0)在增加第0个维度得到(1,C,H,W)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
